Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learning. Existing opponent modeling methods encode intent using an embedding derived from episode information chosen a priori, such as the opponent's next action or a future environment state, and use this to guide the ego-agent's behavior. These approaches assume that the chosen information is universally representative of intent; however, we show empirically that this is not the case as intentions are often task- and environment-dependent. To address this, we introduce a task-adaptive opponent modeling framework that learns a performance-driven mixture of multiple intent representations. We further introduce a new intention representation that maximizes mutual information with the ego-agent's future returns, thereby capturing opponent information that is most directly relevant to performance. Our approach consistently matches or exceeds the performance of state-of-the-art baselines across diverse tasks and yields insights into when and why different opponent modeling strategies succeed.

翻译：对手意图建模对于非合作、竞争及一般和式多智能体强化学习中的有效决策至关重要。现有对手建模方法使用由先验选定的回合信息（例如对手的下一个动作或未来环境状态）生成的嵌入来表示意图，并以此引导自身智能体的行为。这些方法假设选定信息普遍代表意图；然而，我们的实证研究表明事实并非如此，因为意图通常依赖于具体任务和环境。为解决此问题，我们提出了一种任务自适应的对手建模框架，该框架学习基于性能驱动的多意图表示混合。我们进一步引入了一种新的意图表示，它能够最大化与自身智能体未来回报的互信息，从而捕获与性能直接最相关的对手信息。我们的方法在多种任务中始终达到或超越现有最优基线的性能，并揭示了不同对手建模策略成功的情境与原因。